Functional Eating and Strategic Groups in Canada
Bibliographic record
Abstract
The emergence of the nutraceuticals and functional foods (NFFs) products is a challenge to economists and management scientists interested in studying the evolution of the sector. There are profound issues in product and in market classification. This paper reports the results of an exploratory study that illustrates the use of strategic groups as a means to research this emerging industry, using a sample of 280 companies in Canada. The analysis identifies six main strategic groups within the NFFs sector that compete on the market, product and capability strategic fields. The results indicate that the sector is widely heterogeneous in firm composition, markets served, and intragroup and intergroup product strategies. L'émergence de produits nutraceutiques et aliments fonctionnels (NAF) pose un défi aux économistes et aux spécialistes en sciences administratives intéressés par l'étude de I'évolution de ce secteur où perdurent de nomhreux prohlémes de classification de produits et de marchés. Cet article présente les résultats d'une étude exploratoire qui démontre la pertinence de la notion de groupe stratégique dans l'analyse d'un échantillon de 280 compagnies de ce secteur au Canada. Cette etude identifie six groupes strategiques dans le secteur des NAF en concurrence au plan des champs stratégiques de marchés, de produits et de compétences. Les résultats de la recherche montrent que le secteur est largement hétérogène au niveau de la composition des firmes, des marchés desservis et des stratégies de produits intergroupes et intragroupes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".